A Pipeline for Automating Emergency Medicine Documentation Using LLMs with Retrieval-Augmented Text Generation
Accurate and efficient documentation of patient information is vital in emergency healthcare settings. Traditional manual documentation methods are often time-consuming and prone to errors, potentially affecting patient outcomes. Large Language Models (LLMs) offer a promising solution to enhance med...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2519169 |
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